A number of approaches have studied personalized speech recognition, such as for dysarthric and accented speech [1]} by finetuning a subset of layers using labelled data of targeted accents or accent adaptation [2]} using labelled data for the target accent.
[3]} personalizes ASR on-device using RNN Transducers. [4]} works on a problem that corresponds exactly to the reverse of our setting by trying to determine the most error-prone sentences for the model to record utterances on. While this can be effective for user-driven personalization, our method is suited for when we have fixed speech utterances and the only actionable item for us is to transcribe a subset of them.
